tf.feature_column.make_parse_example_spec(feature_columns)
Defined in tensorflow/python/feature_column/feature_column.py
.
Creates parsing spec dictionary from input feature_columns.
The returned dictionary can be used as arg 'features' in tf.parse_example
.
Typical usage example:
# Define features and transformations
feature_a = categorical_column_with_vocabulary_file(...)
feature_b = numeric_column(...)
feature_c_bucketized = bucketized_column(numeric_column("feature_c"), ...)
feature_a_x_feature_c = crossed_column(
columns=["feature_a", feature_c_bucketized], ...)
feature_columns = set(
[feature_b, feature_c_bucketized, feature_a_x_feature_c])
features = tf.parse_example(
serialized=serialized_examples,
features=make_parse_example_spec(feature_columns))
For the above example, make_parse_example_spec would return the dict:
{
"feature_a": parsing_ops.VarLenFeature(tf.string),
"feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
"feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
}
Args:
feature_columns
: An iterable containing all feature columns. All items should be instances of classes derived from_FeatureColumn
.
Returns:
A dict mapping each feature key to a FixedLenFeature
or VarLenFeature
value.
Raises:
ValueError
: If any of the givenfeature_columns
is not a_FeatureColumn
instance.